Adaptive Online Replanning with Diffusion Models

Understand

Diffusion models have risen as a promising approach to data-driven planning, and have demonstrated impressive robotic control, reinforcement learning, and video planning performance.

• Given an effective planner, an important question to consider is replanning -- when given plans should be regenerated due to both action execution error and external environment changes.
• Direct plan execution, without replanning, is problematic as errors from individual actions rapidly accumulate and environments are partially observable and stochastic.
• Simultaneously, replanning at each timestep incurs a substantial computational cost, and may prevent successful task execution, as different generated plans prevent consistent progress to any particular goal.